Kate Culhane, Dept. of Ecology, Evolution, and Marine Biology, UC Santa Barbara
kathrynculhane@ucsb.edu
SUMMARY
Exploring the data
DEPENDENCIES
##### PACKAGES #####
library(tidyverse) # data manipulation & visualization
library(vegan) # community analyses
library(goeveg) # scree plot for NMDS
##### DATA #####
# Invertebrate counts by order from sticky and pitfall trap samples
prey <- read_csv("output/02_prey.csv")
# Invertebrate counts by order from lizard stomach pumping samples
diet <- read_csv("output/02_diet_inverts.csv")
# Site by species matrix (invert prey data)
prey_matrix <- read_csv("output/02_prey_matrix.csv")
# Site by species matrix (diet data)
diet_matrix <- read_csv("output/02_diet_matrix.csv")Abundance by order
Available prey community
# Summary table
prey %>%
group_by(order, trap_type) %>%
summarise(n = sum(count)) %>%
pivot_wider(names_from = trap_type, values_from = n) %>%
mutate(total = pitfall + sticky) %>%
arrange(-total)# Abundance by order
prey %>%
ggplot(aes(x = trap_type, y = count)) +
geom_jitter(aes(color = trap_type),
width = 0.1, size = 1.5) +
geom_boxplot(fill = NA, outlier.shape = NA) +
facet_wrap(~ fct_reorder(order, count, .fun = sum, .desc = TRUE),
scales = "free_y") +
theme_classic() +
theme(legend.position = "NA") +
labs(x = "Trap type", y = "Abundance (indv/trap)")
Diet
# Summary table
diet %>%
group_by(order) %>%
summarise(n = sum(count)) %>%
arrange(-n)# Abundance by order
diet %>%
# Data wrangling
group_by(order) %>%
mutate(n = length(count)) %>%
# Plot
ggplot(aes(x = fct_reorder(order, count, .fun = sum, .desc = TRUE), y = count)) +
geom_jitter(width = 0.1, size = 1.5) +
geom_boxplot(fill = NA, outlier.shape = NA) +
geom_text(aes(y = 80, label = paste("n =", n)),
stat = "unique", angle = 90, size = 3) +
scale_y_log10() +
theme_classic() +
theme(legend.position = "NA",
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
labs(x = "Order", y = "Abundance (indv/lizard)")
NMDS
Invert trap data
# Wrangle matrix
m_p <- as.matrix(prey_matrix[-c(1:4)])
rownames(m_p) <- prey_matrix$sample
m_p_hell <- decostand(m_p, 'hellinger') # Hellinger transformation
m_p_meta <- select(prey_matrix, sample, site, trap, trap_type) # metadata
# Scree plot to check stress per number of dimensions
dimcheckMDS(m_p_hell, distance = "bray", autotransform = FALSE, k = 10)
## Run 0 stress 0.2455473
## Run 1 stress 0.473834
## Run 2 stress 0.4723164
## Run 3 stress 0.2228119
## ... New best solution
## ... Procrustes: rmse 0.01731212 max resid 0.1986803
## Run 4 stress 0.4719769
## Run 5 stress 0.223215
## ... Procrustes: rmse 0.004923924 max resid 0.06419877
## Run 6 stress 0.4720854
## Run 7 stress 0.2228233
## ... Procrustes: rmse 0.0005059689 max resid 0.004540309
## ... Similar to previous best
## Run 8 stress 0.465649
## Run 9 stress 0.2298373
## Run 10 stress 0.2287735
## Run 11 stress 0.2232264
## ... Procrustes: rmse 0.004631167 max resid 0.06102247
## Run 12 stress 0.474459
## Run 13 stress 0.4730141
## Run 14 stress 0.2228789
## ... Procrustes: rmse 0.001190156 max resid 0.01459893
## Run 15 stress 0.2230326
## ... Procrustes: rmse 0.00438998 max resid 0.06209094
## Run 16 stress 0.2358193
## Run 17 stress 0.224727
## Run 18 stress 0.2230406
## ... Procrustes: rmse 0.004531546 max resid 0.06418904
## Run 19 stress 0.2287462
## Run 20 stress 0.2357839
## *** Solution reached
## Run 0 stress 0.1436293
## Run 1 stress 0.147051
## Run 2 stress 0.1479869
## Run 3 stress 0.1542941
## Run 4 stress 0.157293
## Run 5 stress 0.1580501
## Run 6 stress 0.1571429
## Run 7 stress 0.1568249
## Run 8 stress 0.1484989
## Run 9 stress 0.1484684
## Run 10 stress 0.1572966
## Run 11 stress 0.1512325
## Run 12 stress 0.1586262
## Run 13 stress 0.14585
## Run 14 stress 0.1489052
## Run 15 stress 0.1537238
## Run 16 stress 0.1479076
## Run 17 stress 0.1628468
## Run 18 stress 0.1474388
## Run 19 stress 0.1494413
## Run 20 stress 0.152329
## *** No convergence -- monoMDS stopping criteria:
## 1: no. of iterations >= maxit
## 18: stress ratio > sratmax
## 1: scale factor of the gradient < sfgrmin
## Run 0 stress 0.09709427
## Run 1 stress 0.0975036
## ... Procrustes: rmse 0.009769245 max resid 0.1255632
## Run 2 stress 0.09747799
## ... Procrustes: rmse 0.009391054 max resid 0.1240968
## Run 3 stress 0.09919853
## Run 4 stress 0.09755056
## ... Procrustes: rmse 0.01008272 max resid 0.09690611
## Run 5 stress 0.09990082
## Run 6 stress 0.09762724
## Run 7 stress 0.09735131
## ... Procrustes: rmse 0.009532866 max resid 0.0972117
## Run 8 stress 0.09709445
## ... Procrustes: rmse 0.0002246076 max resid 0.002686827
## ... Similar to previous best
## Run 9 stress 0.09744186
## ... Procrustes: rmse 0.009436546 max resid 0.09221591
## Run 10 stress 0.09735411
## ... Procrustes: rmse 0.009369763 max resid 0.09688807
## Run 11 stress 0.1003504
## Run 12 stress 0.09764309
## Run 13 stress 0.0997154
## Run 14 stress 0.09994785
## Run 15 stress 0.09771122
## Run 16 stress 0.09706002
## ... New best solution
## ... Procrustes: rmse 0.002954216 max resid 0.03998377
## Run 17 stress 0.09768524
## Run 18 stress 0.09765987
## Run 19 stress 0.0970586
## ... New best solution
## ... Procrustes: rmse 0.0004263772 max resid 0.00418592
## ... Similar to previous best
## Run 20 stress 0.09730263
## ... Procrustes: rmse 0.004970963 max resid 0.0572948
## *** Solution reached
## Run 0 stress 0.07047178
## Run 1 stress 0.07176096
## Run 2 stress 0.07029202
## ... New best solution
## ... Procrustes: rmse 0.007438677 max resid 0.06143557
## Run 3 stress 0.07025245
## ... New best solution
## ... Procrustes: rmse 0.003279147 max resid 0.02997311
## Run 4 stress 0.07104961
## Run 5 stress 0.07046098
## ... Procrustes: rmse 0.005122288 max resid 0.04941762
## Run 6 stress 0.07103114
## Run 7 stress 0.07102986
## Run 8 stress 0.07038575
## ... Procrustes: rmse 0.004627424 max resid 0.06317074
## Run 9 stress 0.07025476
## ... Procrustes: rmse 0.001174339 max resid 0.01537297
## Run 10 stress 0.07110796
## Run 11 stress 0.07153529
## Run 12 stress 0.07108121
## Run 13 stress 0.07115929
## Run 14 stress 0.07027192
## ... Procrustes: rmse 0.002205914 max resid 0.02496444
## Run 15 stress 0.07113186
## Run 16 stress 0.0711633
## Run 17 stress 0.07025549
## ... Procrustes: rmse 0.001357963 max resid 0.01274137
## Run 18 stress 0.07113844
## Run 19 stress 0.0702544
## ... Procrustes: rmse 0.0003403458 max resid 0.004119722
## ... Similar to previous best
## Run 20 stress 0.07110164
## *** Solution reached
## Run 0 stress 0.05433226
## Run 1 stress 0.05435536
## ... Procrustes: rmse 0.002197148 max resid 0.01414371
## Run 2 stress 0.05446114
## ... Procrustes: rmse 0.008052334 max resid 0.08931407
## Run 3 stress 0.05439223
## ... Procrustes: rmse 0.005707055 max resid 0.07053331
## Run 4 stress 0.05460937
## ... Procrustes: rmse 0.01631002 max resid 0.0819997
## Run 5 stress 0.05448055
## ... Procrustes: rmse 0.005422432 max resid 0.05878164
## Run 6 stress 0.05438171
## ... Procrustes: rmse 0.004596542 max resid 0.05899845
## Run 7 stress 0.05440612
## ... Procrustes: rmse 0.009614012 max resid 0.08976012
## Run 8 stress 0.05442542
## ... Procrustes: rmse 0.00844972 max resid 0.08237777
## Run 9 stress 0.05448582
## ... Procrustes: rmse 0.01591553 max resid 0.08040107
## Run 10 stress 0.05448977
## ... Procrustes: rmse 0.01623271 max resid 0.08167314
## Run 11 stress 0.0544841
## ... Procrustes: rmse 0.00540828 max resid 0.05272724
## Run 12 stress 0.05458539
## ... Procrustes: rmse 0.01177617 max resid 0.09362556
## Run 13 stress 0.05439498
## ... Procrustes: rmse 0.004333892 max resid 0.05062166
## Run 14 stress 0.05438809
## ... Procrustes: rmse 0.006448776 max resid 0.0876939
## Run 15 stress 0.05437136
## ... Procrustes: rmse 0.004723722 max resid 0.06611154
## Run 16 stress 0.0546297
## ... Procrustes: rmse 0.0166029 max resid 0.0820563
## Run 17 stress 0.05446684
## ... Procrustes: rmse 0.006189069 max resid 0.05984547
## Run 18 stress 0.05504664
## Run 19 stress 0.05433407
## ... Procrustes: rmse 0.0008277278 max resid 0.004330551
## ... Similar to previous best
## Run 20 stress 0.0543633
## ... Procrustes: rmse 0.005573439 max resid 0.07822644
## *** Solution reached
## Run 0 stress 0.04561447
## Run 1 stress 0.04576188
## ... Procrustes: rmse 0.008535841 max resid 0.08782132
## Run 2 stress 0.04656548
## Run 3 stress 0.04706558
## Run 4 stress 0.04687136
## Run 5 stress 0.04563521
## ... Procrustes: rmse 0.006278656 max resid 0.08607648
## Run 6 stress 0.04583243
## ... Procrustes: rmse 0.0109735 max resid 0.08013107
## Run 7 stress 0.04600532
## ... Procrustes: rmse 0.009715914 max resid 0.05178756
## Run 8 stress 0.0456371
## ... Procrustes: rmse 0.002504273 max resid 0.02549081
## Run 9 stress 0.04585001
## ... Procrustes: rmse 0.01212621 max resid 0.08465542
## Run 10 stress 0.04578779
## ... Procrustes: rmse 0.01450285 max resid 0.09880397
## Run 11 stress 0.04618146
## Run 12 stress 0.04571355
## ... Procrustes: rmse 0.01091735 max resid 0.09952246
## Run 13 stress 0.04557393
## ... New best solution
## ... Procrustes: rmse 0.007472039 max resid 0.09869587
## Run 14 stress 0.04560337
## ... Procrustes: rmse 0.007219815 max resid 0.09783747
## Run 15 stress 0.04648267
## Run 16 stress 0.04633689
## Run 17 stress 0.04615614
## Run 18 stress 0.04661499
## Run 19 stress 0.04633442
## Run 20 stress 0.04678188
## *** No convergence -- monoMDS stopping criteria:
## 20: no. of iterations >= maxit
## Run 0 stress 0.03951312
## Run 1 stress 0.040215
## Run 2 stress 0.04003901
## Run 3 stress 0.04008733
## Run 4 stress 0.04016194
## Run 5 stress 0.04001916
## Run 6 stress 0.03967042
## ... Procrustes: rmse 0.002769614 max resid 0.02839586
## Run 7 stress 0.04065189
## Run 8 stress 0.04092821
## Run 9 stress 0.04097991
## Run 10 stress 0.04107712
## Run 11 stress 0.03981163
## ... Procrustes: rmse 0.009263066 max resid 0.05685012
## Run 12 stress 0.04003494
## Run 13 stress 0.03961997
## ... Procrustes: rmse 0.004381001 max resid 0.03751196
## Run 14 stress 0.04099444
## Run 15 stress 0.04053131
## Run 16 stress 0.04048298
## Run 17 stress 0.04047862
## Run 18 stress 0.04043364
## Run 19 stress 0.04053737
## Run 20 stress 0.03991251
## ... Procrustes: rmse 0.009682115 max resid 0.1020571
## *** No convergence -- monoMDS stopping criteria:
## 20: no. of iterations >= maxit
## Run 0 stress 0.03474612
## Run 1 stress 0.03493355
## ... Procrustes: rmse 0.008277399 max resid 0.1037568
## Run 2 stress 0.03579384
## Run 3 stress 0.03539244
## Run 4 stress 0.03567962
## Run 5 stress 0.03563182
## Run 6 stress 0.03542742
## Run 7 stress 0.03488527
## ... Procrustes: rmse 0.01352326 max resid 0.1058673
## Run 8 stress 0.03506371
## ... Procrustes: rmse 0.009217526 max resid 0.1031214
## Run 9 stress 0.03551687
## Run 10 stress 0.03595592
## Run 11 stress 0.03499921
## ... Procrustes: rmse 0.005386445 max resid 0.03285242
## Run 12 stress 0.03483764
## ... Procrustes: rmse 0.005669398 max resid 0.04360363
## Run 13 stress 0.03505158
## ... Procrustes: rmse 0.01298077 max resid 0.06586003
## Run 14 stress 0.03566764
## Run 15 stress 0.03493322
## ... Procrustes: rmse 0.008508611 max resid 0.09777426
## Run 16 stress 0.03542118
## Run 17 stress 0.03563835
## Run 18 stress 0.03497099
## ... Procrustes: rmse 0.01098578 max resid 0.09918192
## Run 19 stress 0.03518141
## ... Procrustes: rmse 0.01466542 max resid 0.09990068
## Run 20 stress 0.03509173
## ... Procrustes: rmse 0.009807319 max resid 0.1045895
## *** No convergence -- monoMDS stopping criteria:
## 20: no. of iterations >= maxit
## Run 0 stress 0.03098824
## Run 1 stress 0.03166019
## Run 2 stress 0.03189484
## Run 3 stress 0.03156419
## Run 4 stress 0.03164194
## Run 5 stress 0.03202007
## Run 6 stress 0.03192216
## Run 7 stress 0.03125804
## ... Procrustes: rmse 0.01267131 max resid 0.05966812
## Run 8 stress 0.03156993
## Run 9 stress 0.03161224
## Run 10 stress 0.03191758
## Run 11 stress 0.0315357
## Run 12 stress 0.03153151
## Run 13 stress 0.03133851
## ... Procrustes: rmse 0.01415195 max resid 0.1011115
## Run 14 stress 0.03197711
## Run 15 stress 0.03149894
## Run 16 stress 0.0316026
## Run 17 stress 0.03130238
## ... Procrustes: rmse 0.01106221 max resid 0.05656353
## Run 18 stress 0.03152832
## Run 19 stress 0.03159372
## Run 20 stress 0.03115157
## ... Procrustes: rmse 0.006513012 max resid 0.03358866
## *** No convergence -- monoMDS stopping criteria:
## 20: no. of iterations >= maxit
## Run 0 stress 0.02752796
## Run 1 stress 0.02819517
## Run 2 stress 0.02836264
## Run 3 stress 0.02814889
## Run 4 stress 0.02816129
## Run 5 stress 0.02824675
## Run 6 stress 0.02880824
## Run 7 stress 0.02805976
## Run 8 stress 0.02780216
## ... Procrustes: rmse 0.005694316 max resid 0.03513871
## Run 9 stress 0.0279111
## ... Procrustes: rmse 0.0143536 max resid 0.0841367
## Run 10 stress 0.0281309
## Run 11 stress 0.0278135
## ... Procrustes: rmse 0.01066425 max resid 0.07802394
## Run 12 stress 0.02809477
## Run 13 stress 0.02789513
## ... Procrustes: rmse 0.01640333 max resid 0.08038254
## Run 14 stress 0.02811373
## Run 15 stress 0.02798192
## ... Procrustes: rmse 0.01159462 max resid 0.08616648
## Run 16 stress 0.02817648
## Run 17 stress 0.02826769
## Run 18 stress 0.02854547
## Run 19 stress 0.02829301
## Run 20 stress 0.02825553
## *** No convergence -- monoMDS stopping criteria:
## 20: no. of iterations >= maxit
## [1] 0.22281194 0.14362935 0.09705860 0.07025245 0.05433226 0.04557393 0.03951312 0.03474612 0.03098824
## [10] 0.02752796# Create NMDS ordination
ord <- metaMDS(m_p_hell,
distance = 'bray', # use Bray-Curtis distances
autotransform = FALSE, # already manually transformed the matrix
k = 3, # number of dimensions
trymax = 1000)
# Stress plot
stressplot(ord)
# Plot ordination
scale_trap <- c("red", "orange")
names(scale_trap) <- c("pitfall", "sticky")
par(mar = c(4,4,1,1))
plot(ord, display = 'species', type = 'n')
points(ord, display = 'sites', pch = 19, col = scale_trap[m_p_meta$trap_type])
ordiellipse(ord, groups = m_p_meta$trap_type,
label = TRUE, col = scale_trap, lwd = 2)
text(ord, display = 'species')
K = 3; ordination stress = 0.0970586
Notes & resources
Session info
devtools::session_info()## - Session info ------------------------------------------------------------------------------------------------
## setting value
## version R version 4.1.0 (2021-05-18)
## os Windows 10 x64
## system i386, mingw32
## ui RStudio
## language (EN)
## collate English_United States.1252
## ctype English_United States.1252
## tz America/Los_Angeles
## date 2021-07-27
##
## - Packages ----------------------------------------------------------------------------------------------------
## ! package * version date lib source
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## P jsonlite 1.7.2 2020-12-09 [?] CRAN (R 4.1.0)
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## P stringi 1.6.2 2021-05-17 [?] CRAN (R 4.1.0)
## P stringr * 1.4.0 2019-02-10 [?] CRAN (R 4.1.0)
## P testthat 3.0.4 2021-07-01 [?] CRAN (R 4.1.0)
## P tibble * 3.1.2 2021-05-16 [?] CRAN (R 4.1.0)
## P tidyr * 1.1.3 2021-03-03 [?] CRAN (R 4.1.0)
## P tidyselect 1.1.1 2021-04-30 [?] CRAN (R 4.1.0)
## P tidyverse * 1.3.1 2021-04-15 [?] CRAN (R 4.1.0)
## P usethis 2.0.1 2021-02-10 [?] CRAN (R 4.1.0)
## P utf8 1.2.1 2021-03-12 [?] CRAN (R 4.1.0)
## P vctrs 0.3.8 2021-04-29 [?] CRAN (R 4.1.0)
## P vegan * 2.5-7 2020-11-28 [?] CRAN (R 4.1.0)
## P viridis 0.6.1 2021-05-11 [?] CRAN (R 4.1.0)
## P viridisLite 0.4.0 2021-04-13 [?] CRAN (R 4.1.0)
## P withr 2.4.2 2021-04-18 [?] CRAN (R 4.1.0)
## P xfun 0.24 2021-06-15 [?] CRAN (R 4.1.0)
## P xml2 1.3.2 2020-04-23 [?] CRAN (R 4.1.0)
## P yaml 2.2.1 2020-02-01 [?] CRAN (R 4.1.0)
##
## [1] C:/Users/kathr/Documents/git-repos/lizard-guts-naxos/renv/library/R-4.1/i386-w64-mingw32
## [2] C:/Users/kathr/AppData/Local/Temp/RtmpKk1eNg/renv-system-library
##
## P -- Loaded and on-disk path mismatch.